大模型工具Ollama

官网:https://ollama.com/
Ollama是一个用于部署和运行各种开源大模型的工具;
它能够帮助用户快速在本地运行各种大模型，极大地简化了大模型在本地运行的过程。用户通过执行几条命令就能在本地运行开源大模型，如Lama 2等;
综上，Ollama是一个大模型部署运行工具，在该工具里面可以部署运行各种大模型，方便开发者在本地搭建一套大模型运行环境;

下载:https://ollama.com/download

下载Ollama
说明:Ollama的运行会受到所使用模型大小的影响;
1、例如，运行一个7B(70亿参数)的模型至少需要8GB的可用内存(RAM)，而运行一个13B(130亿参数)的模型需要16GB的内存，33B(330亿参数)的型需要32GB的内存;
2、需要考虑有足够的磁盘空间，大模型的文件大小可能比较大，建议至少为Ollama和其模型预留50GB的磁盘空间3、性能较高的CPU可以提供更好的运算速度和效率，多核处理器能够更好地处理并行任务，选择具有足够核心数的CPU:
4、显卡(GPU):Ollama支持纯CPU运行，但如果电脑配备了NVIDIA GPU，可以利用GPU进行加速，提高模型的运行速度和性能;

命令行使用ollama 打开终端，输入 ollama -h,查看到所有的命令

service ollama start启动allama

输入ollama -v查看当前版本，能输出版本则安装成功

运行模型单行对话

拉取并运行llama2模型
ollama run llama2
直接输入该命令会检查目录下是否有该模型，没有会自动下载，下载好后自动运行该模型
其他模型见library (ollama.com)

# 查看 Ollama 版本
ollama -v

# 查看已安装的模型
ollama list

# 删除指定模型
ollama rm [modelname]

# 模型存储路径
# C:\Users\<username>\.ollama\models

ollama run qwen:0.5b

默认Ollama api会监听11434端口，可以使用命令进行查看netstat -ano |findstr 114341

//加依赖
<dependency>
<groupld>org.springframework,ai</groupld>
<artifactld>spring-ai-ollama-spring-boot-starter</artifactld>
</dependency>
//写代码
注入OllamaChatClient
@Resource
private OllamaChatClient ollamaChatClient,
//调用call方法
ollamaChatClient.call(msg);

完整pom文件

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>3.3.0</version>
        <relativePath/> <!-- lookup parent from repository -->
    </parent>
    <groupId>com.zzq</groupId>
    <artifactId>spring-ai-ollama</artifactId>
    <version>0.0.1-SNAPSHOT</version>
    <name>spring-ai-ollama</name>
    <description>spring-ai-ollama</description>
    <properties>
        <java.version>17</java.version>
        <!--        快照版本-->
        <spring-ai.version>1.0.0-SNAPSHOT</spring-ai.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-devtools</artifactId>
            <scope>runtime</scope>
            <optional>true</optional>
        </dependency>
        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <optional>true</optional>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>
    </dependencies>
    <dependencyManagement>
        <dependencies>
            <dependency>
                <groupId>org.springframework.ai</groupId>
                <artifactId>spring-ai-bom</artifactId>
                <version>${spring-ai.version}</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency>
        </dependencies>
    </dependencyManagement>

    <build>
        <plugins>
            <plugin>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-maven-plugin</artifactId>
                <configuration>
                    <excludes>
                        <exclude>
                            <groupId>org.projectlombok</groupId>
                            <artifactId>lombok</artifactId>
                        </exclude>
                    </excludes>
                </configuration>
            </plugin>
        </plugins>
    </build>
    <!--    快照版本-->
    <repositories>
        <repository>
            <id>spring-snapshot</id>
            <name>Spring Snapshots</name>
            <url>https://repo.spring.io/snapshot</url>
            <releases>
                <enabled>false</enabled>
            </releases>
        </repository>
    </repositories>
</project>

application文件内容

spring:
  application:
    name:spring-ai-05-ollama
  ai:
    ollama:
      base-url: http://localhost:11434
      chat:
        options:
          model: qwen:0.5b

controller

package com.zzq.controller;

import jakarta.annotation.Resource;
import org.springframework.ai.ollama.OllamaChatModel;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;

@RestController
public class OllamaController {
   @Resource
    private OllamaChatModel ollamaChatModel;
   @RequestMapping(value = "/ai/ollama")
    public Object ollama(@RequestParam(value = "msg")String msg){
       String called=ollamaChatModel.call(msg);
       System.out.println(called);
       return called;
   }
}

package com.zzq.controller;

import jakarta.annotation.Resource;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.ollama.OllamaChatModel;
import org.springframework.ai.ollama.api.OllamaOptions;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;

@RestController
public class OllamaController {
   @Resource
    private OllamaChatModel ollamaChatModel;
   @RequestMapping(value = "/ai/ollama")
    public Object ollama(@RequestParam(value = "msg")String msg){
       String called=ollamaChatModel.call(msg);
       System.out.println(called);
       return called;
   }
    @RequestMapping(value = "/ai/ollama2")
    public Object ollama2(@RequestParam(value = "msg")String msg){
        ChatResponse chatResponse=ollamaChatModel.call(new Prompt(msg, OllamaOptions.create()
                .withModel("qwen:0.5b")//使用哪个大模型
                .withTemperature(0.4F)));//温度，温度值越高，准确率下降，温度值越低，准确率上升
        System.out.println(chatResponse.getResult().getOutput().getContent());
        return chatResponse.getResult().getOutput().getContent();
    }
}